Application versus network layer multicasting in ad hoc networks: the ALMA routing protocol

Application layer multicasting has emerged as an appealing alternative to network layer multicasting in wireline networks. Here, we examine the suitability of application layer multicast in ad hoc networks. To this effect, we propose a flexible receiver-driven overlay multicast protocol which we call Application Layer Multicast Algorithm (ALMA). ALMA constructs an overlay multicast tree in a dynamic, decentralized and incremental way. First, ALMA is receiver-driven: the member nodes find their connections according to their needs. Second, it is flexible, and thus, it can satisfy the performance goals and the needs of a wide range of applications. Third, it is highly adaptive: it reconfigures the tree in response to mobility or congestion. In addition, our protocol has the advantages of an application layer protocol: (a) simplicity of deployment, (b) independence from lower layer protocols, and (c) capability of exploiting features such as reliability and security that may be provided by the lower layers. Through extensive simulations, we show that ALMA performs favorably against the currently best application layer and network layer protocols. In more detail, we find that ALMA performs significantly better than ODMRP, a network layer, for small group sizes. We conclude that the application layer approach and ALMA seem very promising for ad hoc multicasting.     

DESENT: decentralized and distributed semantic overlay generation in P2P networks

The current approach in web searching, i.e., using centralized search engines, rises issues that question their future applicability: 1) coverage and scalability, 2) freshness, and 3) information monopoly. Performing web search using a P2P architecture that consists of the actual web servers has the potential to tackle those issues. In order to achieve the desired performance and scalability, as well as enhancing search quality relative to centralized search engines, semantic overlay networks (SONS) connecting peers storing semantically related information can be employed. The lack of global content/topology knowledge in a P2P system is the key challenge in forming SONS, and this paper describes an unsupervised approach for decentralized and distributed generation of SONS (DESENT). Through simulations and analytical cost models we verify our claims regarding performance, scalability, and quality     

On broadcasting with cooperative diversity in multi-hop wireless networks

Cooperative diversity facilitates spatio-temporal communications without requiring the deployment of physical antenna arrays. While physical layer studies on cooperative diversity have been extensive, higher layer protocols which translate the achievable reduction in the SNR per bit for a given target BER, into system wide performance enhancements are yet to mature. The challenge is that appropriate higher layer functions are needed in order to enable cooperative diversity at the physical layer. We focus on network-wide broadcasting with the use of cooperative diversity in ad hoc networks. We design a novel distributed network-wide broadcasting protocol that takes into account the physical layer dependencies that arise with cooperative diversity. We perform extensive simulations that show that our protocol can outperform the best of the noncooperative broadcasting protocols by: (a) achieving up to a threefold increase in network coverage and, (b) by decreasing the latency incurred during the broadcast by about 50%. We also construct an analytical model that captures the behavior of our protocol. Furthermore, we show that computing the optimal solution to the cooperative broadcast problem is NP-complete and construct centralized approximation algorithms. Specifically, we construct an O(N ^epsi)-approximation algorithm with a computational complexity of O(N4/epsi); we also construct a simpler greedy algorithm.. The costs incurred with these algorithms serve as benchmarks with which one can compare that achieved by any distributed protocol     

Multi-platform data integration in microarray analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.     

Different Types of Chronic Inflammation Engender Distinctive Immunosenescent Profiles in Affected Patients

Immunosenescence encompasses a spectrum of lymphocyte phenotypic alterations. The aim of the study was to evaluate immunosenescent effect of two different forms of chronic inflammation, Systemic Lupus Erythematosous (SLE), a systemic autoimmune disease, and End-Stage Kidney Disease (ESKD), a chronic inflammatory disorder. Certain lymphocyte surface molecules, including CD31, CD45RA, CCR7, CD28, CD57, for T, and IgD, CD27 for B lymphocytes, were analyzed by flow cytometry in 30 SLE and 53 ESKD patients on hemodialysis (HD), and results were compared to 31 healthy controls (HC) of similar age, gender, and nationality. Significant Lymphopenia was evident in both SLE and ESKD-HD patients, compared to HC, affecting B cells 75.4 (14.4–520.8), 97 (32–341), and 214 (84–576) cells/μL, respectively, p < 0.0001, and CD4 cells 651.2 (71.1–1478.2), 713 (234–1509), and 986 (344–1591) cells/μL, respectively, p < 0.0001. The allocation of B cell subpopulations was remarkably different between SLE and ESKD-HD patients. SLE showed a clear shift to senescence (CD19IgD-CD27−) cells, compared to ESKD-HD and HC, 11.75 (10)% vs. 8 (6) vs. 8.1 (10), respectively. Regarding T lymphocytes, Central Memory CD8 cells predominated in both SLE and ESKD-HD patients compared to HC, 53 (50)%, 52 (63), and 24 (64)%, respectively, while ESKD-HD but not SLE patients also had increased expression of CD4CD28− and CD8CD28− cells. In conclusion, both diseases are followed by significant lymphopenia; however, the senescent phenomenon affects the B lymphocyte compartment in SLE patients and T lymphocytes in ESKD-HD patients.     

Effect of freeze–dried kefir culture on proteolysis in feta-type and whey-cheeses

The effect of freeze–dried kefir culture on the proteolysis of feta-type and whey-cheese was investigated. All nitrogen fractions increased during ripening. Although no significant differences were observed in total nitrogen (TN), the levels of water-soluble nitrogen (WSN), pH 4.4-soluble nitrogen (SN), 12% trichloroacetic acid-soluble nitrogen (TCA-SN) and phosphotungstic acid-soluble nitrogen (PTA-SN) were significantly higher in cheeses produced by freeze–dried kefir culture during the later stages of ripening. Content of total free amino acids (FAA) was significantly affected by freeze–dried kefir starter culture and it was continuously increased in kefir-cheese while, in rennet-cheese it was increased up to 30 days of ripening and then slightly decreased. On the other hand, FAA content continuously decreased in kefir-whey-cheese whereas it increased in whey-cheese. The cheese samples produced by freeze–dried kefir as starter culture were characterised as high-quality products during the preliminary sensory evaluation and they were accepted by the panel. Overall, the use of freeze–dried kefir suggested acceleration of cheese ripening and resulted in improved sensory characteristics.     

Using statistical data for reliable mobile communications

We examine the extent to which statistical mobility information can increase the reliability of the service experienced by users in mobile networks. Interrupted or dropped calls are an aspect of reliability that stems from the mobility of users. An existing user can move to a cell where there are no resources available to support their call. A natural solution is the reservation of resources in multiple cells that the user is likely to move to. This scheme is called selective reservations and it relies on predicting the next move of the user. Recently, there has been some work on estimating the movement probabilities (also known as the mobility profile) of the user. In this paper, we quantify the usefulness of the mobility profile to improve the reliability of the service perceived by the mobile users. We identify two parameters which characterize the profile: Accuracy and Focus. Accuracy expresses the probability that the host will move as we expect it to. Focus describes how well we can identify patterns in the movement of the users. In our simulations, we examine the effect of the quality of the predictions on the performance of the system. We show that Accuracy and Focus have great impact on the performance of selective reservations. We also show how flexibility in hand‐offs can help in decreasing the dropping probability, and how this can be facilitated by letting the users make a second try at moving in case it fails the first time. Copyright © 2001 John Wiley & Sons, Ltd.